Korean Journal of Radiology

  • 제23권1호
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  • Pages.6-9
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  • 2022
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  • 1229-6929(pISSN)
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  • 2005-8330(eISSN)
대한영상의학회 (The Korean Society of Radiology)
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Looking Ahead to 2022 for the Korean Journal of Radiology

  • Seong Ho Park (Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center)
  • 투고 : 2021.11.05
  • 심사 : 2021.11.05
  • 발행 : 2022.01.01
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참고문헌

  1. Bluemke DA. Editor's note: 2020-a year like no other for radiology. Radiology 2021;298:243-244  https://doi.org/10.1148/radiol.2020209028
  2. Park SH. What's new in the Korean Journal of Radiology in 2021. Korean J Radiol 2021;22:1-4  https://doi.org/10.3348/kjr.2020.1429
  3. Lane DL, Neelapu SS, Xu G, Weaver O. COVID-19 vaccine-related axillary and cervical lymphadenopathy in patients with current or prior breast cancer and other malignancies: cross-sectional imaging findings on MRI, CT, and PET-CT. Korean J Radiol 2021;22:1938-1945  https://doi.org/10.3348/kjr.2021.0350
  4. Ashoor A, Shephard J, Lissidini G, Nicosia L. Axillary adenopathy in patients with recent Covid-19 vaccination: a new diagnostic dilemma. Korean J Radiol 2021;22:2124-2126  https://doi.org/10.3348/kjr.2021.0635
  5. Kim PH, Kim M, Suh CH, Chung SR, Park JE, Kim SC, et al. Neuroimaging findings in patients with COVID-19: a systematic review and meta-analysis. Korean J Radiol 2021;22:1875-1885  https://doi.org/10.3348/kjr.2021.0127
  6. Purkayastha S, Xiao Y, Jiao Z, Thepumnoeysuk R, Halsey K, Wu J, et al. Machine learning-based prediction of COVID-19 severity and progression to critical illness using CT imaging and clinical data. Korean J Radiol 2021;22:1213-1224  https://doi.org/10.3348/kjr.2020.1104
  7. Weikert T, Rapaka S, Grbic S, Re T, Chaganti S, Winkel DJ, et al. Prediction of patient management in COVID-19 using deep learning-based fully automated extraction of cardiothoracic CT metrics and laboratory findings. Korean J Radiol 2021;22:994-1004  https://doi.org/10.3348/kjr.2020.0994
  8. Marchiori E, Penha D, Nobre LF, Hochhegger B, Zanetti G. Differences and similarities between the double halo sign, the chest CT target sign and the reversed halo sign in patients with COVID-19 pneumonia. Korean J Radiol 2021;22:672-676  https://doi.org/10.3348/kjr.2020.1150
  9. Velasquez-Rimachi V, Chavez-Malpartida SS, Velasquez-Fernandez R, Campos-Ramirez L. Chest X-ray for follow-up of hospitalized COVID-19 patients in settings with limited access to computed tomography. Korean J Radiol 2021;22:864-866  https://doi.org/10.3348/kjr.2020.1356
  10. Bluemke DA, Moy L, Bredella MA, Ertl-Wagner BB, Fowler KJ, Goh VJ, et al. Assessing radiology research on artificial intelligence: a brief guide for authors, reviewers, and readers-from the radiology editorial board. Radiology 2020;294:487-489  https://doi.org/10.1148/radiol.2019192515
  11. Park SH, Choi J, Byeon JS. Key principles of clinical validation, device approval, and insurance coverage decisions of artificial intelligence. Korean J Radiol 2021;22:442-453  https://doi.org/10.3348/kjr.2021.0048
  12. Park HJ, Shin Y, Park J, Kim H, Lee IS, Seo DW, et al. Development and validation of a deep learning system for segmentation of abdominal muscle and fat on computed tomography. Korean J Radiol 2020;21:88-100  https://doi.org/10.3348/kjr.2019.0470
  13. Lee KC, Lee KH, Kang CH, Ahn KS, Chung LY, Lee JJ, et al. Clinical validation of a deep learning-based hybrid (Greulich-Pyle and modified Tanner-Whitehouse) method for bone age assessment. Korean J Radiol 2021;22:2017-2025  https://doi.org/10.3348/kjr.2020.1468
  14. Yoo SJ, Yoon SH, Lee JH, Kim KH, Choi HI, Park SJ, et al. Automated lung segmentation on chest computed tomography images with extensive lung parenchymal abnormalities using a deep neural network. Korean J Radiol 2021;22:476-488  https://doi.org/10.3348/kjr.2020.0318
  15. Pickhardt PJ, Summers RM, Garrett JW. Automated CT-based body composition analysis: a golden opportunity. Korean J Radiol 2021;22:1934-1937  https://doi.org/10.3348/kjr.2021.0775
  16. Ahn Y, Yoon JS, Lee SS, Suk HI, Son JH, Sung YS, et al. Deep learning algorithm for automated segmentation and volume measurement of the liver and spleen using portal venous phase computed tomography images. Korean J Radiol 2020;21:987-997  https://doi.org/10.3348/kjr.2020.0237
  17. Kim DW, Kim KW, Ko Y, Park T, Lee J, Lee JB, et al. Effects of contrast phases on automated measurements of muscle quantity and quality using CT. Korean J Radiol 2021;22:1909-1917  https://doi.org/10.3348/kjr.2021.0105
  18. Kwon JH, Lee SS, Yoon JS, Suk HI, Sung YS, Kim HS, et al. Liver-to-spleen volume ratio automatically measured on CT predicts decompensation in patients with B viral compensated cirrhosis. Korean J Radiol 2021;22:1985-1995  https://doi.org/10.3348/kjr.2021.0348
  19. Lee JG, Kim H, Kang H, Koo HJ, Kang JW, Kim YH, et al. Fully automatic coronary calcium score software empowered by artificial intelligence technology: validation study using three CT cohorts. Korean J Radiol 2021;22:1764-1776  https://doi.org/10.3348/kjr.2021.0148
  20. Lee K, Shin Y, Huh J, Sung YS, Lee IS, Yoon KH, et al. Recent issues on body composition imaging for sarcopenia evaluation. Korean J Radiol 2019;20:205-217  https://doi.org/10.3348/kjr.2018.0479
  21. Lee J, Jeong WK, Kim JH, Kim JM, Kim TY, Choi GS, et al. Serial observations of muscle and fat mass as prognostic factors for deceased donor liver transplantation. Korean J Radiol 2021;22:189-197  https://doi.org/10.3348/kjr.2019.0750
  22. Cho YH, Do KH, Chae EJ, Choi SH, Jo KW, Lee SO, et al. Association of chest CT-based quantitative measures of muscle and fat with post-lung transplant survival and morbidity: a single institutional retrospective cohort study in Korean population. Korean J Radiol 2019;20:522-530  https://doi.org/10.3348/kjr.2018.0241
  23. Sim JS, Baek JH. Unresolved clinical issues in thermal ablation of benign thyroid nodules: regrowth at long-term follow-up. Korean J Radiol 2021;22:1436-1440  https://doi.org/10.3348/kjr.2021.0093
  24. Gormly KL. High-resolution T2-weighted MRI to evaluate rectal cancer: why variations matter. Korean J Radiol 2021;22:1475-1480  https://doi.org/10.3348/kjr.2021.0560
  25. Park SH, Han K, Park SY. Mistakes to avoid for accurate and transparent reporting of survival analysis in imaging research. Korean J Radiol 2021;22:1587-1593  https://doi.org/10.3348/kjr.2021.0579
  26. Park SH. Introducing "recommendation and guideline" of the Korean Journal of Radiology. Korean J Radiol 2021;22:1929-1933  https://doi.org/10.3348/kjr.2021.0785
  27. Hwang EJ, Goo JM, Yoon SH, Beck KS, Seo JB, Choi BW, et al. Use of artificial intelligence-based software as medical devices for chest radiography: a position paper from the Korean Society of Thoracic Radiology. Korean J Radiol 2021;22:1743-1748  https://doi.org/10.3348/kjr.2021.0544
  28. Park BK, Shen SH, Fujimori M, Wang Y. Thermal ablation for renal cell carcinoma: expert consensus from the Asian conference on tumor ablation. Korean J Radiol 2021;22:1490-1496 https://doi.org/10.3348/kjr.2020.1080